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The Effects of Roundup® on Progression of Parkinson\u27s Disease Symptoms in a Mouse Model
No full textGlyphosate-Based Herbicides (GBH) are the most widely used herbicides worldwide. Concerns about the effects of GBH on human health have grown, specifically pointing towards a connection between chronic exposure and neurodegenerative diseases like Parkinson’s disease (PD). PD is characterized by the accumulation of phosphorylated alpha synuclein (aSyn). Previous rodent experiments have shown that introduction of aSyn pre-formed fibrils (PFFs) can induce progressive accumulation of PD-like pathology. We explored the effects of chronic, low-dose GBH exposure in a mouse model of PD. We induced PD-like pathology in male and female wildtype mice by injecting PFFs into the striatum. A control group received saline injections. Half of the mice received drinking water with a low dose of Roundup® (0.075% w/v); the other half received standard drinking water. Motor function, learning and memory were assessed at 1-, 3-, and 6-months post injection. We hypothesized that PFF-injected mice would show impaired learning and memory, and that GBH exposure would exacerbate these deficits and induce alterations in saline-injected mice. Lastly, we hypothesized that males would be more affected than females. Our results show that GBH increased body weight in both males and females, but induced sex-dependent effects in anxiety-like behavior and hippocampus-dependent memory
What was I Made from (Angle 1)
No full texthttps://scholarworks.boisestate.edu/mfa_images_2025/1031/thumbnail.jp
Improved Snow Distribution Estimates Using a Rapid-Response LiDAR and Photogrammetry System
No full textSnow plays a critical role in global hydrology, climate systems, and human activities, particularly in mountainous regions where it is a primary source of freshwater, influences the energy balance, and impacts mobility and commerce. Despite its importance, accurately mapping and predicting snow distribution remains a major challenge due to the complex spatial and temporal variability of snowpack and the lack of an ideal observation system. This dissertation aims to advance our monitoring capability, understanding, and prediction capacity of snow distribution using Light Detection and Ranging (LiDAR) and photogrammetry techniques.
In chapter 2, I discussed the high-resolution LiDAR-derived datasets of multiple sites in the Western United State that I created, contributing to addressing the scarcity of distributed snow depth data in mountain regions. These datasets, collected during NASA\u27s SnowEx campaigns, provide crucial benchmarks for validating emerging snow monitoring techniques across varied environmental conditions. In chapter 3, I introduced Ice-road-copters (IRC), an open-source Python toolkit designed to automate the processing of LiDAR and photogrammetry point clouds for snow depth mapping, significantly reducing the manual labor traditionally required for such tasks. This toolkit streamlines noise removal, ground segmentation, digital elevation model coregistration, and raster differencing, facilitating more efficient and consistent production of snow depth maps.
In Chapter 4, I explored the potential of leveraging snow distribution patterns to predict snow depth across diverse mountain environments in the western United States. I assessed the repeatability of snow distribution patterns and systematically evaluated how prediction accuracy varies with different pattern types, training data quantities, and spatial scales. Results demonstrate high correlation (r \u3e 0.8) of distribution patterns for snow depths exceeding 0.5 m, while shallow snow conditions during early accumulation or late melt exhibit reduced pattern correlation. Prediction performance is optimized when using temporally consistent patterns—accumulation patterns for pre-peak predictions and ablation patterns for post-peak predictions—yielding mean root mean square errors between 0.2-0.4 m across all sites. Notably, robust predictions can be achieved with as few as 10 observations over a 38 km² area, though prediction confidence improves with increased sampling. Performance degrades with larger spatial extents, with errors approximately doubling when scaling from 38 km² to 3,741 km².
Finally, in Chapter 4, I investigated the integration of LiDAR and photogrammetry for enhanced snow monitoring, demonstrating how combining LiDAR\u27s high accuracy with photogrammetry\u27s cost-efficiency could advance operational snow mapping in mountain watersheds. Results show that both Airborne Laser Sanning (ALS) and Unoccupied Aerial Vehicles (UAV) LiDAR provide similar levels of vertical accuracy when validated against reference measurements, with RMSE values of about 15cm. In contrast, photogrammetry exhibited substantially higher uncertainty that increase with vegetation density—from 27 cm in sparse vegetation to over 1m in dense vegetation—highlighting a critical limitation of this approach for comprehensive watershed monitoring. I proposed an enhanced methodology that combines vegetation masking during photogrammetric processing with gap-filling based on historical LiDAR-derived snow distribution patterns. This integrated approach reduces the RMSE of photogrammetric snow depth maps from 44 cm to 27 cm, demonstrating the potential for synergistic combination of these technologies.
In summary, this dissertation addresses critical gaps in snow science by providing: (1) Invaluable datasets for understanding snow distribution and validating emerging snow monitoring techniques across varied environmental conditions, (2) open-source tools that democratize LiDAR and photogrammetry point cloud processing capabilities, (3) practical guidelines for leveraging limited observations and distribution pattern for predicting snow depth (4) integrated methodologies that maximize the strengths of LiDAR and photogrammetry technologies
From Molecular Crystals to Catalytic Surfaces: Computational Approaches to Complex Systems
No full textUnderstanding and modeling complex systems is one of the core challenges in modern science. Whether it be the intricate interactions within molecular crystals, the evolving mechanisms of bacterial resistance, or the dynamics of catalytic surfaces, accurately representing these systems is essential for scientific progress. This dissertation addresses these challenges by focusing on advances in computational modeling, demonstrating how various methods such as Density Functional Theory (DFT), machine learning, and custom-developed methods can simplify and help predict complex behaviors across multiple domains. By applying and understanding the scope of these methods to each field, this work underscores how computational modeling bridges the gap between intricate physical phenomena and practical scientific applications.
The first section employs DFT and terahertz (THz) spectroscopy to study the vibrational properties of molecular crystals, emphasizing the adaptation of frequency scaling techniques to address temperature-induced volumetric changes in DFT calculations. This approach enhances our understanding of polymorphic behaviors and the use of computational techniques for facilitating the interpretation of THz spectra in molecular crystals.
The second section addresses the urgent need for new antibiotics due to rising antibiotic resistance. It demonstrates how machine learning and in silico methods expedite the drug discovery process, utilizing large datasets to predict drug efficacy and model bacterial resistance mechanisms efficiently. This segment also considers the potential integration of quantum computing to further accelerate antibiotic development by exploring complex chemical spaces.
The final section presents a novel approach to addressing the complexities of catalytic surface interactions. In heterogeneous catalysis, the identification of active sites and the understanding of surface interactions are critical for optimizing reactions. By automating the generation of local symmetry-invariant descriptors and identification of unique sites, this dissertation shows how computational models can reduce reliance on intuition and arbitrary decision-making, thereby facilitating systematic automation of high throughput screening for the discovery of more efficient catalysts across a wide range of materials.
Overall, this dissertation underscores the critical role of advanced computational methods in understanding and modeling complex systems in materials science, biology, and chemistry. By bridging the gap between theoretical and practical scientific applications, it highlights the pivotal contributions of computational modeling to future scientific discoveries
Advancing Snow Water Equivalent Monitoring with Machine Learning and L-Band Interferometric Synthetic Aperture Radar (InSAR) Data
No full textSeasonal snow is a critical freshwater resource for an estimated 2 billion people worldwide. Yet, accurately measuring the amount of water sitting in a snowpack, referred to as snow water equivalent (SWE), over large, often mountainous regions has posed a long-standing challenge. Ground-based measurements of SWE are precise but sparse, while remote sensing techniques like passive microwave sensors struggle with coarse resolution and signal saturation in deep snow. Due to the challenges of direct SWE measurement, snow depth has emerged as an alternative pathway to SWE estimation. SWE is strongly correlated with snow depth, and by leveraging this relationship, we can estimate SWE from snow depth measurements if the snowpack bulk density is known. Snow depth has been successfully measured with remote sensing techniques, such as light detection and ranging (lidar). However, the high cost of lidar prevents its widespread adoption, necessitating an alternative remote sensing approach (e.g., active microwave). This dissertation explored the potential of L-band Interferometric Synthetic Aperture Radar (InSAR), which can be deployed over large areas, and machine learning (ML) to estimate snow depth at various spatial resolutions. Additionally, we developed an ML model to estimate snow density from snow depth and variables that can be readily recorded or derived from the date and location of depth observations (i.e., snow class, day of water year, and elevation). This approach reduces the SWE estimation problem to a snow depth monitoring problem combined with a snow density modeling effort. Results indicated that this approach is promising and may complement existing snow monitoring practices
The Burning Earth
No full textSunil Amrith, Renu and Anand Dhawan Professor of History, Professor of the Environment, Yale University - Sunil Amrith twins the stories of environment and Empire, of genocide and eco-cide, of an extraordinary expansion of human freedom and its planetary costs. Drawing on an extraordinarily rich diversity of primary sources, he reckons with the ruins of Portuguese silver mining in Peru, British gold mining in South Africa, and oil extraction in Central Asia. He explores the railroads and highways that brought humans to new terrains of battle against each other and against stubborn nature. Amrith’s account of the ways in which the First and Second World Wars involved the massive mobilization not only of men, but of other natural resources from around the globe, provides an essential new way of understanding war as an irreversible reshaping of the planet. So too does his book reveal the reality of migration as consequence of environmental harm. The imperial, globe-spanning pursuit of profit, joined with new forms of energy and new possibilities of freedom from hunger and discomfort, freedom to move and explore, has brought change to every inch of the Earth.https://scholarworks.boisestate.edu/ideas_of_nature_gallery/1043/thumbnail.jp
Intramuscular Injection Guideline Revisions are Needed Based on Body Mass Index, Needle Length, Sex, and Skin to Muscle Depth
No full textBackground: Medications and vaccines are delivered via intramuscular injection throughout the world to improve health and to prevent or treat illness. Intramuscular injections are an essential nursing and pharmacist skill which includes choosing an appropriate injection site and selecting needle lengths and gages based on weight and muscle mass. Scientific evidence suggests changes in current practice and national guidelines to fully reach medication and vaccine efficacy.
Results: There is a direct relationship between medication/vaccine efficacy and health outcomes. The variance in weight provides vital information on the needle length and site of injection. Body mass index, age, sex, and skin to muscle depth are important measures when choosing the right needle length and site for an intramuscular injection.
The literature draws attention to deltoid injection concerns. The standard 25 mm needles may not be appropriate for small or large and obese populations as there can be reduced efficacy if the medication or vaccine is not deposited into the muscle. Other concerns include medications and vaccines deposited in the shoulder bursa in some patients instead of the deltoid pad causing pain, limited range of motion, and long-lasting disabilities.
Dorsogluteal and ventrogluteal sites are used for deep gluteal muscle injections. The ventrogluteal is the most appropriate site for deep muscle injections yet many nurses prefer the dorsogluteal site because of preference/comfort with the procedure. Deep gluteal injections often require longer needles to reach the muscle mass in large or obese patients, especially women.
Intramuscular injection guidelines are followed by clinicians worldwide. Agencies/centers such as the Centers for Disease Control and Prevention update these guidelines as needed but most national guidelines lack specific intramuscular injection instructions. Clinicians usually perform clinical procedures taught in basic training and follow national guidelines rather than performing individual clinical assessments.
Conclusion: Improving public health is vital, especially in various weighted populations that may not be receiving optimal efficacy of medications and vaccines. Updates to guidelines and continuous education and training in injection techniques should be a priority in all health agencies worldwide
Functional Data Analysis of Weekly Confirmed COVID-19 Cases per Million in Relation to Unemployment Rates and Emergency Declaration Responses
No full textAt the onset of the COVID-19 pandemic, U.S. states implemented policies that attempted to mitigate the spread and effects of the disease, such as a state of emergency declaration. This study explores the application of functional data analysis (FDA) as a means to investigate the impact of U.S. state-level emergency declarations on weekly confirmed COVID-19 cases per million during the first year of the pandemic. We focus on the duration and extent to which proactive states showed significantly different patterns of COVID-19 spread than those that implemented policies reactively. Additionally, we examine patterns in monthly state-level unemployment rates over the same study period. Utilizing FDA techniques— including data smoothing, functional principal component analysis (FPCA), functional canonical correlation analysis, and functional t- and F-tests— we found that the patterns in cases per million were significantly different between proactive and reactive states from 2 to 10 weeks. Using k-means clustering, states were clustered into high unemployment rate and low unemployment rate groups based on their FPCA scores. We found that cases per million in states with consistently higher unemployment rates during the first year of the pandemic significantly differed from that of states with lower unemployment rates from 2 to 8 weeks and from 27 to 38 weeks. Finally, states were classified into one of four groups based on both their emergency declaration response (proactive or reactive) and unemployment rate (higher or lower). We found that there was an effect of the four classification groups on cases per million curves at 52 weeks
Enhancing Forensics and Safety Analysis with Confocal Raman Microscopy
No full textConfocal Raman Microscopy is an advanced, non-destructive analytical technique that has promising potential in both forensic science and material safety research. It allows for chemical specificity, rapid analysis, and the ability to preserve the integrity of samples making it an ideal method for applications where repeated accurate identification is crucial. This research investigates two areas where confocal Raman microscopy offers significant improvements: forensic trace evidence analysis and the investigation of fabric flammability in the presence of contaminants. In the forensic context, confocal Raman microscopy is becoming a reliable technique used to detect and identify illicit substances at crime scenes. Traditional methods of drug detection can involve destructive techniques that risk contaminating or compromising residue amounts of evidence. Confocal Raman microscopy combined with the forensic tape-lifting method can provide an efficient way to collect trace drug residues and detect compounds present at a scene. In material safety, confocal Raman microscopy is used to examine how hairspray residues interact with fabric fibers such as silk, cotton, wool, nylon, and polyester. When exposed to heat, these interactions may increase flammability risks. By analyzing the chemical changes induced by hairspray, potential contribution to fire hazard on clothing can be detected. Together, these applications demonstrate the versatility and provide advantages for using confocal Raman Microscopy for complex sample analysis
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